{"doi":"10.1002/mrm.29995","title":"Optimizing background suppression for dual‐module velocity‐selective arterial spin labeling: Without using additional background‐suppression pulses","abstract":"PURPOSE: Background suppression (BS) is recommended in arterial spin labeling (ASL) for improved SNR but is difficult to optimize in existing velocity-selective ASL (VSASL) methods. Dual-module VSASL (dm-VSASL) enables delay-insensitive, robust, and SNR-efficient perfusion imaging, while allowing efficient BS, but its optimization has yet to be thoroughly investigated. METHODS: The inversion effects of the velocity-selective labeling pulses, such as velocity-selective inversion (VSI), can be used for BS, and were modeled for optimizing BS in dm-VSASL. In vivo experiments using dual-module VSI (dm-VSI) were performed to compare two BS strategies: a conventional one with additional BS pulses and a new one without any BS pulse. Their BS performance, temporal noise, and temporal SNR were examined and compared, with pulsed and pseudo-continuous ASL (PASL and PCASL) as the reference. RESULTS: The in vivo experiments validated the BS modeling. Strong positive linear correlations (r > 0.82, p < 0.0001) between the temporal noise and the tissue signal were found in PASL/PCASL and dm-VSI. Optimal BS can be achieved with and without additional BS pulses in dm-VSI; the latter improved the ASL signals by 8.5% in gray matter (p = 0.006) and 12.2% in white matter (p = 0.014) and tended to provide better temporal SNR. The dm-VSI measured significantly higher ASL signal (p < 0.016) and temporal SNR (p < 0.018) than PASL and PCASL. Complex reconstruction was found necessary with aggressive BS. CONCLUSION: Guided by modeling, optimal BS can be achieved without any BS pulse in dm-VSASL, further improving the ASL signal and the SNR performance.","journal":"Magnetic Resonance in Medicine","year":2024,"id":453909,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9535,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":491839,"name":"Jia Guo","orcid":"0000-0003-3371-5857","position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-19T02:03:03.428151Z","pmid":"38173296","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}